AI Fluency knowledge

Verification and Information Literacy

Verification and Information Literacy is one of eight AIGW AI Fluency capabilities for responsible AI-enabled work, development, and implementation.

D3 / Capability guide

What it means

Test AI-assisted claims against original sources, dates, methods, assumptions, conflicting evidence, and uncertainty.

Why it matters in AI-enabled work

AI can make unsupported claims sound convincing. Verification helps prevent errors from moving into decisions before anyone sees them.

What strong practice looks like

A strong practitioner matches checking depth to the consequence of the claim. They assess source authority, method, date, corroboration, limitations, and what remains uncertain.

Common failure modes

  • Treating a fluent answer as evidence that a claim is true.
  • Checking links or summaries without examining the original source or method.
  • Keeping an unsupported claim because it makes the recommendation easier to present.

Development practices

  1. 1Record the source, date, method, assumption, uncertainty, and use/revise/remove decision for one important claim.
  2. 2Use a primary-source checklist for consequential recommendations.
  3. 3Keep a short correction log so the team can see what changed and why.

Relationship to the assessment

This capability is one dimension in the AIGW AI Fluency & Career Growth Assessment. The assessment offers developmental interpretation based on its current coded responses and scenario evidence; this public guide does not reveal items, answer keys, scoring weights, or private report logic.

Explore the assessment